【作者】 徐元进；

【导师】 胡光道；

【作者基本信息】 中国地质大学 ， 地球探测与信息技术， 2009， 博士

【摘要】 遥感是使用某种装置(如航空、航天传感器)，不直接接触被研究目标，通过电磁波获取目标物数据，并对获取的数据进行分析处理从而得到所需信息的一门科学和技术。传统的多光谱遥感存在着一些局限，比如波段少、波谱宽、信息粗糙等。在找矿中多光谱遥感最大的不足是光谱分辨率低，这是影响地物光谱不能有效区分的原因。随着遥感技术发展，高光谱遥感应运而生，它提供了继承多光谱找矿的方法和发展新方法技术的机遇。高光谱遥感在电磁波谱的可见光和红外波段内光谱分辨率可达10 nm或更高，能获取许多非常窄的近似连续的光谱数据，它的这种独特性能，特别是在地表物质的分类、识别等方面具有明显的优势。它使得宽波段遥感中不可探测的物质，在高光谱遥感中能被探测，是20世纪80年代以来人类在对地观察方面所取得的重大技术突破之一，是当前遥感前沿技术。成像光谱仪(高光谱传感器)的发展是为了获取图像数据，并在许多窄的波段范围内利用矿物诊断性吸收特征进行矿物识别填图。目前，已在运营的一些典型航空成像光谱仪(AVIRIS，HyMap，CASI，MAIS等)和第一个星载高光谱成像仪(Hyperion)已成功进行了大量地表矿物识别填图工作。尽管人们最先认识到高光谱遥感技术重要性的是地质领域，但是现在它已应用到包括地质在内的各个方面，比如岩矿识别、土地变化监测、植被类型区分等。本研究以有一定植被覆盖下云南省中甸普朗斑岩铜矿区及其外围区域为研究区，在分析研究普朗区域地质背景及其区内典型矿床——普朗斑岩型铜矿床特征的基础上，使用美国EO-1卫星Hyperion的高光谱图像数据、野外实测光谱数据以及当前常用光谱库USGS、JPL、JHU等中光谱数据，研究野外环境效应下岩矿光谱的稳定特征，探寻一套面向找矿的高光遥感岩矿信息提取技术方法流程。主要研究内容及结论有：1．对EO-1 Hyperion高光谱遥感数据预处理进行了研究，对图像中像元绝对辐射值转换、坏线修复及垂直条纹去除、大气校正和几何纠正等进行了处理，并实现了坏线修复及垂直条纹去除等算法。2．分析研究区蚀变矿物光谱特征，采用光谱角度制图法SAM(Spectral AngleMapping)，从高光谱遥感图像上提取了褐铁矿、黄铁矿、伊利石、高岭石、白云母、蒙脱石、绿泥石、透闪石、白云石和方解石等蚀变矿物信息。但是，这些蚀变信息明显地集中在地苏嘎-浪都-卓玛一带，大部分都在4200米以上，植被覆盖相对较少；其它区域识别效果不好，特别是植被影响严重的区域。分析认为：使用实验室环境下标准矿物光谱作为参考光谱进行识别存在缺陷，根源在于在野外混合光谱中矿物端元光谱表现复杂。同时研究中发现：蚀变信息提取时，阀值的存在给结果带来不确定性，需要通过一定方式表达像元光谱匹配的程度，便于对结果进行取舍。3．通过分析不同影响因素下野外岩矿光谱变化特征，得到野外光谱定量描述的稳定特征参数：光谱曲线整体或部分形态和典型吸收谷位置(波长)。利用研究区野外地物光谱，对基于光谱向量的两种常用方法——光谱角余弦和相关系数的应用表明：大部分地物基本上与各自的最接近地物有较高的度量值；光谱角余弦的区分精度不如相关系数高。在野外岩矿光谱稳定特征参数研究基础上，通过对基于光谱吸收特征的相似性度量方法的研究，认为：野外岩矿光谱吸收特征识别，可从典型吸收谷位置(波长)进行定位分析。4．从包含各种矿物信息的野外岩石光谱出发，提出了基于穷举法的高光谱遥感图像地物识别方法，并进行了实现。该过程中关键技术方法有：光谱数据库及其管理、包络线去除归一化处理方法、识别结果成图及其分析功能等。该识别方法通过提供像元光谱和对应参考光谱的相关系数灰度图，以显示像元识别的匹配程度，并结合使用识别信息RGB图及其分析功能，对参考光谱进行取舍，这样避免了参考光谱选取的局限性。对研究区遥感图像未进行包络线去除归一化处理的识别结果分析后表明：选取的14种参考光谱是适合的，识别结果是满意的；经野外18种地物检验后认为，无论是相关性差的地物，还是相关性好的地物，识别结果与实际情况基本相同。试验区外A、B区域的识别结果研究表明：只要参考光谱中包含了该区域的相关地物光谱，就一定能识别出每一像元的最接近物；只是由于环境条件等(如植被、云雪)影响，造成匹配程度的相关系数不同。同时可知：野外环境因素(包括植被和土壤)对岩石光谱的影响是大的，因此在找矿中对蚀变岩的进行识别时，考虑野外环境因素的影响是必须的。包络线去除归一化处理，能突出了地物光谱特征信息，尤其是对波形起伏较大的植被更加有效，但对于光谱波形较平缓的地物不太明显。对遥感图像进行包络线去除归一化处理后的识别结果分析表明：在岩石等地物信息增强的同时，图像的背景噪声也被增强，造成识别结果不理想。因此，对环境背景噪声高的高光谱图像，采用包络线去除归一化处理，不利于岩矿信息的提取。5．为了精确识别光谱整体形态极其相似的地物，在基于穷举法的高光谱遥感图像地物识别基础上，增加了吸收谷位置的匹配，形成了基于光谱整体形态和局部吸收特征的高光谱遥感图像识别方法的流程。吸收谷位置匹配中关键技术主要有：吸收谷位置偏移范围的确定、吸收谷位置的光谱匹配等。吸收谷位置偏移范围的确定包括如下三步：①使用交叉相关光谱匹配方法，分析野外典型地物的所有光谱，获取光谱整体的最大偏移量；②在包络线去除法算法基础上，实现了吸收谷位置(波长)的自动获取。通过对比分析野外地物的所有光谱中，对应着相同矿物或离子的吸收谷位置(波长)，获取吸收谷位置的最大偏移量；③在分析两个最大偏移量的基础上，确定吸收谷位置偏移范围。吸收谷位置的光谱匹配，是通过两项规则实现的：①参考光谱和像元光谱对应的吸收谷位置的波长差在吸收谷位置偏移范围之内；②以两光谱对应的吸收谷位置为中心，在参考光谱的每一个吸收谷波段范围内，计算参考光谱和像元光谱的相关系数，并保证该值大于指定的阀值。五种野外代表性地物光谱的偏移结果研究表明：研究区一般地物光谱曲线整体错位都较小，局部吸收谷位置的偏移主要由自身位置漂移引起的。识别应用结果表明：编号为R11～R13的三个地物和各自对应的参考光谱，在整体光谱形态上有较小的相关性，但是它们和各自对应参考光谱有高度一致的局部吸收特征，为识别结果的准确性提供了依据，避免了单纯通过光谱整体形态进行识别的不足。6．对基于普朗斑岩铜矿区地物光谱的矿区高光谱遥感图像识别结果进行分析，认为该结果具有较高准确性的原因在于：基于光谱整体形态和局部吸收特征的识别方法考虑到了地物内在物理化学性质及外在光谱形态；使用的野外地物光谱具有该矿区环境下的特征，适合于矿区遥感图像的识别。使用14种矿区野外地物光谱作为参考光谱，对矿区及外围区域遥感图像进行识别，分析两区识别结果中不同相关系数范围内像元数百分含量情况，认为矿区野外地物光谱能用于外围区域的高光谱遥感图像识别，且准确性较高；野外实地检验，也证实了识别结果的准确性。对比14种(矿区)地物光谱、18种(矿区及其外围)地物光谱进行矿区及其外围遥感图像的识别结果，研究表明：普朗斑岩型铜矿区野外蚀变岩光谱可用于外围区域的高光谱遥感图像上蚀变信息识别，这为利用典型矿区野外蚀变岩光谱进行未勘探区蚀变信息识别提供了依据。同时，得到了基于野外地物光谱的高光谱遥感图像蚀变信息图，从图中信息可知，除了地苏嘎-浪都-卓玛-比都一带蚀变信息明显外，地苏嘎-松诺-普朗一带蚀变信息也很明显，而且与斑岩体吻合较好；欧赛拉、阿热蚀变信息较明显，与斑岩体也存在一定程度的吻合，这些与我们在野外考查的结果是一致的。主要创新点有：1．通过分析不同影响因素下野外岩矿光谱变化特征，得到野外光谱定量描述的稳定特征参数：光谱曲线整体或部分形态和典型吸收谷位置(波长)。2．建立了基于穷举法的高光谱遥感图像地物识别方法。该方法通过提供像元光谱和对应参考光谱的相关系数灰度图，以显示像元识别的匹配程度，并结合使用识别信息RGB图及其分析功能，对参考光谱进行取舍，这样避免了参考光谱选取的局限性。3．提出了基于光谱整体形态和局部吸收特征的高光谱遥感图像识别流程。4．证明了普朗斑岩型铜矿区野外蚀变岩光谱可用于外围区域的高光谱遥感图像上蚀变信息识别，这为利用典型矿区野外蚀变岩光谱进行未勘探区蚀变信息识别提供了依据。目前，在矿产勘查遥感应用领域，以高光谱图像数据和地物光谱数据为基础，开展了信息提取技术研究。光谱建模、光谱匹配和地质填图技术，是当前矿产勘查高光谱遥感领域的重点和热点。本研究就是在此背景下进行的相关工作研究。光谱的吸收特征参数，包括：吸收位置、吸收深度、吸收宽度、吸收对称性等，本研究从野外光谱定量描述的相对稳定特征参数：光谱曲线整体或部分形态和典型吸收谷位置(波长)出发，对面向找矿的高光谱遥感岩矿信息提取方法进行了研究。本研究使用高光谱遥感数据实现了面向找矿的高光遥感岩矿信息提取的技术方法，该技术方法已在本研究区得到了一定程度的检验，有待于在其它区域被检验，并能得到推广应用。更多还原

【Abstract】 Remote sensing (RS) is the science of acquiring, processing, and interpreting images and related data (acquired from aircrafts and satellites) that record the interaction between matter and electromagnetic energy. Broad-band multispectral RS has some limitations, such as less band amount, wide band width and rough spectral information expression. Thus a major problem in mineral exploration using multispectral sensors is the insufficient spectral resolution, which doesn’t exhibit subtle differences in spectral signatures. The advent of new hyperspectral sensor technology, in terms of both sensor and technique development, has provided opportunity to revisit previous RS approaches to mineral exploration as well as development of improved methods.Hyperspectral RS can provide spectral information of materials usually over several hundreds of narrow contiguous spectral bands, with high spectral resolution on the order of 10 nm or narrower in the visible and infrared wavelengths. As such, hyperspectral data allow identification of specific materials, whereas multispectral data only allow discrimination between classes of materials. As a result, hyperspectral data have obvious advantages in ground object classification and identification that is done by quantitative comparison of known reference spectra (laboratory or field spectra) to unknown image spectra. Thus hyperspectral RS has been one of the most important earth-observing technologies since the 1980s and is a current advanced technology of RS. Imaging spectrometers (hyperspectral sensors) were developed to acquire image data in many narrow spectral bands so that diagnostic absorption features of minerals which are typically 20-40 nm in width could be identified in image spectra to enable mapping. At present several operational airborne imaging spectrometer systems (AVIRIS, HYMAP, CASI, MAIS, etc.) and the first spaceborne hyperspectral sensor (Hyperion) successfully allow surface mineralogical mapping. Despite the fact that the geology community was the first to recognize the importance of hyperspectral technology, nowadays hyperspectral technology has been applied to many fields, including rock and mineral identification, land cover change monitoring, and vegetation type identification, etc.The study area is located in the Pulang porphyry copper deposit and its periphery area, Zhongdian County of Yunan province, China. Hyperspectral image data of this area, from the American satellite EO-1’s Hyperion, was acquired in November, 2003. The field spectra were collected with FieldSpectral Pro, an Analytical Spectral Devices (ASD) spectrometer. Some mineral spectra used come from the current famous spectra libraries, such as USGS、JPL and JHU. To accomplish prospecting-oriented approaches to information extraction of rocks and minerals using hyperspectral RS data, this paper analyses the regional geological background, and district geological features of the study area, and then studies some steady absorption feature characteristics of field rocks and minerals spectra. The main research results are as follows:1. The preprocessing of EO-1 Hyperion hyperspectral data includes: converting Hyperion data to absolute radiance, restoring bad lines and removing vertical stripes, correcting for atmospheric scattering and correcting geometric distortions. The hyperspectral data of the study area have been successfully preprocessed and the methods of bad lines’ restoral and vertical stripes’ removal are implemented with the aid of a VC++ programe.2. Alteration minerals in the study area mainly include limonite, pyrite, illite, kaolinite, muscovite, montmorillonite, chlorite, tremolite, calcite and dolomite. To extract alteration information from the hyperspectra image, the spectral features of these minerals are analyzed, and their laboratory spectra and the method of SAM (Spectral Angle Mapping) are used. The extracted alteration information is obviously concentrated in Are-Pulang-Langdu area which is at altitude of above 4200 meters and has little vegetation coverage. But in other areas, especially some vegetation covering areas, alteration information can’t be availably extracted. The results show that these used laboratory minerals spectra aren’t very appropriate to spectra matching with image spectra because minerals spectra aren’t steady under the influence of field environment and noisy surface properties of ground objects. At the same time, the uncertainty of the used thresholds in information extraction often results in different results, thus a map is indispensably used to demonstrate match degree between each pixel spectrum and its reference spectrum so that the results can be easily accepted or rejected.3. Spectral variation characteristics of rocks and minerals under the influence of different field environments are analyzed, and the steady spectral characteristics (spectral overall shape and absorption-band position) are obtained. The two methods based on spectral vector: SAM and SCM (Spectral Correlation Mapping) are applied to field ground object spectra of the study area. The results indicated that most objects have a great matching value with their similar objects, and SAM has a smaller identification degree than SCM. Spectral similarity measures based on absorption features are studied, and the result shows that the identification of field rocks and minerals based on spectral absorption features can be done by quantitative absorption-band positions’ comparison of known reference spectra (laboratory or field spectra) to unknown image spectra.4. This paper presents a new approach to object identification of hyperspectral image based on the spectral exhaustive method (EXM), and the approach has been implemented. The key techniques and methods in this approach include: spectral library and its management, the normalizing processing of continuum removal, and Mapping and analyzing functions of identification results. Mapping of identification results is accomplished using a color (RGB) map and a gray-grade map. The color map is generated using the reference spectra with different colors so that all pixels can be shown as the identified type. The gray-grade map is created based on the correlation coefficient between each pixel spectrum and its reference spectrum, which demonstrates the degree of match. These two maps are evaluated in order to select suitable reference spectra for the study and judge the accuracy of the object identification results.The approach was applied to an EO-1 Hyperion hyperspectral image of the study area. The results indicated that the fourteen reference spectra are suitable for this study and the identification results are satisfactory. In eighteen field ground objects examined, although some of their corresponding pixels have a small correlation coefficient, the identified objects corresponding to the pixels are basically identical to the field ground objects. The identification results of A and B area (outside of experiment area) show that as long as the reference spectra are associated with object spectra in the area, each pixel spectrum of the area can find out its most similar reference spectrum. But some correlation coefficients in the gray map are small because of the influence of field environment (vegetation, cloud, snow etc.). On the other hand, it is known that field objects (such as vegetation, soil) have a great influence on rock spectra. Thus it is necessary to think about environment factors which influence identification of alteration rocks in prospecting.The continuum-removed spectra can enhance their absorption features, and it is especially obvious to most vegetation spectra except for the object spectra with flat overall shape. The identification results of the continuum-removed image show that the normalizing processing of continuum removal isn’t advantageous to the rocks’ identification in the hyperspectral image with intensive environment information.5. In order to more accurately identify ground objects with similar spectral overall shape, it is important to analyze the absorption-band positions. This paper presents a development in EXM which is mainly based on analysis of the absorption-band positions, and get a flow for object identification based on the spectral overall shape character and local absorption feature. The key techniques and methods in the identification include: determination of absorption-band offset rang, and spectral matching algorithm of absorption-band position. Determination of absorption-band offset range is done via three steps:①For each known ground object, many spectra are obtained in the field. The cross correlogram spectral matching (CCSM) is used to acquire the maximal overall offset from these spectra.②The algorithm of absorption-band positions automatically obtained is implemented based on the method of continuum removal. The absorption-band positions (corresponding to the same material) in these spectra are analyzed to acquire the maximal position offset.③An offset range is got on the basis of the maximal overall offset and the maximal position offset. This offset range is taken as a constraint on the matching process. Spectral matching of absorption-band position is accomplished by satisfying the two criterias:①Wavelength difference between the corresponding absorption-band positions in the two spectra is within the offset range.②The correlation coefficient between the two spectra, calculated within each absorption-band width of the reference spectrum, is greater than a specified value.The offset results of five typical field object spectra show that overall offsets of object spectra are small in the study area and the offset of absorption-band position is mainly caused by the offset of the position. The application results show that the three ground objects (their serial numbers are R11, R12 and R13) have a small a small correlation coefficient in the overall shape between them and their reference spectra, but their similarity in absorption features (including positions of absorption bands) can provide valuable information for the identification of objects. Thus the method of object identification based on the spectral overall shape character and local absorption feature can get more accurate identification results than the method based on spectral overall shape alone.6.The identification results of Pulang porphyry copper deposit area using this area’s field spectra are thought to be a more accurate identification results, for which there are two resasons: on one hand, the method based on the spectral overall shape character and local absorption feature takes the object’s physical and chemical properties, and spectral overall shape into consideration. On the other hand, the field spectra own the characteristics of Pulang porphyry copper deposit area, thus they are suitable for the object identification of this area. The hyperspectral image of Pulang porphyry copper deposit area and its periphery area is identified using the fourteen spectra, and then percents of the two areas’ pixel numbers within different correlation coefficients are analyzed. The results show that the fourteen field spectra can be used in the identification of periphery area and the examination of the actual objects indicated that the identification is effective.The hyperspectral image of Pulang porphyry copper deposit area and its periphery area is identified by dividually using fourteen field spectra (deposit area) and eighteen field spectra (deposit area and its periphery area), then the identification results indicated that field alteration rocks in the Pulang porphyry copper deposit area can be used in the identification of periphery area, which can provide a clue to alteration information extracted in unknown area using the field spectra of known typical deposit area. At the same time, alteration information map of the study area is obtained. The results shown in the map indicated that there is much obvious alteration information in the Disua-Langdu-Zhuoma-Bidu area and Disua-Songnuo-Pulang area which are in accord with the areas of porphyry bodies in geological map of the study area, and there is obvious alteration information in Ousaila area and Are area which are almost in accord with the areas of porphyry bodies. All the alteration information can be found in the field examination.The main innovations of this study are as follows:1. The steady spectral characteristics (spectral overall shape and absorption-band position) are obtained by analyzing spectral variation characteristics of field rocks and minerals.2. This paper presents a new approach to object identification of hyperspectral image based on the spectral exhaustive method, and the approach helps us select suitable reference spectra for the study and judge the accuracy of the object identification results.3. This paper presents a development in EXM which is based on analysis of mainly the absorption-band positions, and get a flow for object identification based on the spectral overall shape character and local absorption feature.4. It is proved that the field alteration rocks in the Pulang porphyry copper deposit area can be used in the identification of periphery area, which can provide a clue to alteration information extracted in unknown area using the field spectra of known typical deposit area.Nowadays some methods of information extraction are being studied using hyperspectral image and object spectra in the application of hyperspectral RS to the mineral exploration. Spectral modeling, spectral matching and geological mapping become important and hot techniques in the application. Absorption-band parameters include the position, depth, width, and asymmetry of the feature, etc. This paper obtains steady spectral characteristics of field spectra in the study: spectral overall shape and absorption-band position, and then studies the prospecting-oriented approaches to information extraction of rocks and minerals. This research accomplishs the prospecting-oriented approaches to information extraction of rocks and minerals using hyperspctral RS data, these approaches are effective in this study area and will be tested in other areas for a wider application.更多还原